What questions have you stopped asking because nobody else is looking to answer, or simply can’t?

It’s time to rethink the interfaces between man and machine. As the boundaries dissolve, we can increasingly decode us. Psyfr has developed the `first non imaging human presence detection systems based upon the data signature of a human being. We don’t need to `see you` anymore.

HPD Human Presence Detection

HPD fully automated system scans Lorries for human presence. Machine learning algorithm predicts human presence and its location(s). Exception reporting alerts security personnel.

Contactless, frictionless, nonstop detection means Trade is not slowed. Non intrusive, passive, all weather outdoor system affords cost saving over slower labour intensive technologies.

HPD does not rely upon a function of the human to reveal itself i.e. breathing, heat or smell; just that you are there. System can be easily integrated in Command Centre and operatives.

Uncouple your operational reliance and cost upon a human interpreting a fuzzy image on screen,  we no longer need to see you….

If you require more information. Stuart@Psyfr.ai

Digital twinning, data pictures and digitizing goods.

Same scan, many benefits.

By  re tooling HPD we can also scan the entire the cargo taking a data picture, noting number and location of pallets; so what leaves A, goes through C, and arrives at B is untouched, digital twinning providing assurance of proof of delivery.

Digitizing goods automatically enables x checking to the manifest, only alerting when there is a significant deviation from the `data signature` of these goods. HPD can identify and recognise broad categories of goods i.e. if the manifest states sawn timber products and pallets of bottled water there has to be a strong correlation to our ML prediction.

Unusualness, making sense of what is hidden from us.

Passive RF characterisation of Satellites and random space objects.

Does the Satellite do what it say’s on the tin, or is it doing other stuff?

Measuring unusualness or novelty data in a continuous data stream to understand the characteristics of individual Satellites.

Find out what’s going on up there.

Pax Trax

Main difficulties in Airports today are the inexorable rise in passengers and resulting congestion. The oxymoron, is that it`s becoming more difficult to simplify the passenger journey, optimise resources, and maximise NAR in sync.

Airport Terminals strive to become resource optimised processing boxes, but to make that work and improve pax UX, requires ever increasing intelligence and data insight to support sophisticated operational decisions.

Pax Trax provide the answers you didn’t know existed to your most valuable commercial and operational questions. We capture and analyse “difficult to get” information, because getting more of the same data isn’t going to solve it.

Real Time Queue Management

The Challenge

Simple binary reporting of average queue times and poor performance SLA breach’s, is not what good looks like. It’s changed. It’s had to. Now it’s about trying to lessen the number of variable outcomes; good, is managing a consistent and efficient  optimised security processing box, matching staff resource to demand slot peaks improving pax UX and NAR and hopefully, in sync.

The Process

Our data collection toolkit [Pax Trax] captures location data, but also matched to their flight destination(s), available airside dwell time and impact upon NAR; and provide a near 100% reliable SLA breach alert.

The KPI dashboard refresh rate is 180 seconds and not based upon data sampling to provide an “average” queue time, because, averages lie.

Operationally relevant our KPI dashboard features an Experiment Suite that aks, could that “operational decision” been made differently? And for that read better and what would have been the outcome? Unique data function also include RCT Random Control Tests affords managers the opportunity of comparing the performance of two completely flight processing performance side by side measuring all key data input variables.

The Results

Pax Trax accurately measures queue times for passengers, and operations by “process point” aligned to your SLA. Combined with sophisticated predictive analytical techniques for capacity forecasting we have improved several major UK Airports departure security processing functions through:

Reduced SLA Breach`s

Reduced “average processing cost per pax per hour” by 5%.

Increased pax flow rate through security channel(s) by 6% through optimisation and pax behavioural segmentation.

Capacity Forecasting, Flow Management
and Resource Optimisation

The Challenge

The disconnect between what’s forecast and what really happens is because historical data is a poor indicator of future events; compounded by  weak statistical analysis and “shallow data sets” are insufficient to make confident decisions.

Confidence in capacity and passenger flow forecasts start strong, but confidence evaporates when it plays out in the terminal. With fixed assets and staff rotas the only “flex” in the terminal is a lengthening queue time. With a baked in the cake xxx pax per hour processing rate per channel you rely on a constant uniform pax flow even in demand slot peaks.

The most variable factor is the queue time. Every decision feeds into that. The resource optimisation needle will only “flicker on the dial” if your pax forecasts can be relied upon. There is a direct performance correlation between the representativeness of predicted flow rate and your ability to plan resources to process those passengers. That`s the unlock.

The process

Forecasting needs to be uncoupled from historical trend analysis as the basis of resource allocation and operational decisions. Machine learning, predictive analytics and adopting time series analysis to produce daily forecasts with additional contextual data inputs will provide management with a heightened confidence. It`s about decision support and confidence in the numbers day after day.

The results

A 3.5% saving in operating costs in demand slot peaks, based upon average processing cost per pax per hour in a UK Top 10 Airport.

Experiment Suite & Data Science

An Experiment Suite takes the guesswork out of large scale implementations by allowing you to plan structured experiments throughout the Airport to test hypotheses and theories in a scientific way. The experiment suite allows you to run a series of scenarios to establish whether a better ROI could have been delivered.

Data Science

Key to achieving maximising value from the data generated every day in your airport are the data science and artificial intelligence algorithms used in modern business. Our Data Science Hub provides a suite of tools integrating popular algorithms and packages into a single environment. Rather than transferring vast quantities of data between databases and software stacks, the Data Science Hub provides everything required for analysis.

Asynchronous data capture

We have uniquely perfected an asynchronous data capture system which sync`s and merges data (automatically) between say a passenger’s smart device and i.e. an ABCR gate or epos till. This process unlocks value from previously un measurable data insights.

We can significantly exceed and infill your gaps in purchasing behaviour and spending by passengers once airside. This cascade of previously unobtainable data unlocks opportunities to improve UX and NAR. No longer rely on historical WDF epos data, average dwell time and guess work.

By x tabbing data we construct socio demographic cluster groups that exhibit certain characteristics allowing you to understand how these pax, consume, interact and behave by flight, TOD, destination etc. This could for example allow you to adopt variable pricing models if using digital screen in F&B and incentivise spend?

F+B spend analysis by flight

The missing jigsaw piece in measuring NAR is attributing F&B spend to individual flights. Unlike WDF sales, airports cannot apportion spend to individual pax as there is no way of knowing end destination, there is no “identifier”. Our system uses the common denominator (smart device) to which we attach some of the flight details at the ABCR gates.

Tokenisation ensures privacy. With an additional epos black box connected to the restaurant till we can sync purchases to mobile knowing its flight destination. The spend data captured provides the same details as printed on till receipt. We provide the ground truth, itemised spend in F&B, by flight not average spend or guess work.

Life Time Value (LTV) Modelling

LTV Modelling measures the “value” to the airport of individual passenger group clusters. The “value” is the complete sum of all the important data inputs that affect the passenger`s journey and goes way beyond the typically narrow sub set available to airports.

The LTV model measures and ranks entirely new data sets that were previously unobtainable challenging deeply held anecdotal beliefs or observational data that somehow are indelibly ingrained into airports understanding.

The model has many inputs including loyalty (to the airport), not based upon spurious “satisfaction” mkt research (unreliable), but considering the true catchment area of your current passengers.

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Case Studies

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